Outcomes Research in Physical Therapy

Jennifer Ferrell Pleiman (University of Louisville, USA)
Copyright: © 2010 |Pages: 172
EISBN13: 9781609603021|DOI: 10.4018/978-1-61520-723-7.ch007
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This research investigates the outcomes of physical therapy by using data fusion methodology to develop a process for sequential episode grouping data in medicine. By using data fusion, data from different sources will be combined to review the use of physical therapy in orthopedic surgical procedures. The data that were used to develop sequential episode grouping consisted of insurance claims data from the Thomson Medstat MarketScan database. The data will be reviewed as a continuous time lapse for surgery date; that is, the utilization of physical therapy for a defined time period both before and after surgery will be used and studied. The methodology of this research will follow a series of preprocessing cleaning and sequential episode grouping, culminating in text mining and clustering the results to review. Through this research, it was found that the use of physical therapy for orthopedic issues is not common and was utilized in under 1% of the data sampled. Text mining was further utilized to examine the outcomes of physical rehabilitation in cardiopulmonary research. The functional independence measures score at discharge can be predicted to identify the potential benefits of physical rehabilitation on a patient by patient basis. By text mining and clustering comorbidity codes, the severity of those clusters were used in a prediction model to determine rehabilitation benefits. Other information such as preliminary functional independence scores and age (in relation to independence scores) were used in the prediction model to provide the prescribing physician a way to determine if a patient will benefit from rehabilitation after a cardiopulmonary event.
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